Ethical Intelligence by Design: Bias Mitigation through Nature-Inspired Machine Learning

Abstract

AI and data mining methods have become the focus of quite a few industries, however, the issue of ethical responsibility, especially in terms of algorithmic biases has come to the fore. This paper seeks to investigate the severe ramifications of the biased data which causes applications in areas like recruitment, law enforcement or healthcare to be biased. However, some existing literature addresses the issues in a rather shallow manner when it comes to methodologies of identifying or addressing bias and its instances. This puts the public trust at risk and limits the advancements of AI technologies.As a solution to this urgent problem, this research proposes a rigorous framework for the detection, response and prevention of bias in all stages of the data mining process, and explores a range of algorithms and metrics for the identification and control of bias in machine learning models. Several case studies allow us to investigate the applicability of the proposed solutions and evaluate their effectiveness in practice, thus increasing the level of fairness and the result of the research demonstrated relationships in order to reduce bias not only in anticipation of legal requirements but as part of ethical practice of AI. This research is concerned with the discussion of ethics of algorithm use in regards to AI applications to equity and inclusion among the stakeholders of AI allowing for actionable insights into how different actors will interact with the technology.

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